Comparative Molecular Characterization and Pharmacokinetics of IgG1-Fc and Engineered Fc Human Antibody Variants to Insulin-like Growth Factor 2 Receptor (IGF2R)
Bibliographic record
Abstract
Novel therapeutic approaches are much needed for the treatment of osteosarcoma. Targeted radionuclide therapy (TRT) and radioimmunotherapy (RIT) are promising approaches that deliver therapeutic radiation precisely to the tumor site. We have previously developed a fully human antibody, named IF3, that binds to insulin-like growth factor 2 receptor (IGF2R). IF3 was used in TRT to effectively inhibit tumor growth in osteosarcoma preclinical models. However, IF3's relatively short half-life in mice raised the need for improvement. We generated an Fc-engineered version of IF3, termed IF3δ, with amino acid substitutions known to enhance antibody half-life in human serum. In this study, we confirmed the specific binding of IF3δ to IGF2R with nanomolar affinity, similar to wild-type IF3. Additionally, IF3δ demonstrated binding to human and mouse neonatal Fc receptors (FcRn), indicating the potential for FcRn-mediated endocytosis and recycling. Biodistribution studies in mice showed a higher accumulation of IF3δ in the spleen and bone than wild-type IF3, likely attributed to abnormal spleen expression of IGF2R in mice. Therefore, the pharmacokinetics data from mouse xenograft models may not precisely reflect their behavior in canine and human patients. However, the findings suggest both IF3 and IF3δ as promising options for the RIT of osteosarcoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".